论文标题
招聘广告中的技能要求：基于工资回归中解释能力的技能分类方法的比较
Skill requirements in job advertisements: A comparison of skill-categorization methods based on explanatory power in wage regressions
论文作者
论文摘要
在本文中，我们比较了不同的方法来从求职广告中提取技能要求。我们考虑了三种基于专家创建的关键字词典的自上而下方法，以及一种无监督主题建模的自下而上的方法，即潜在的Dirichlet分配（LDA）模型。我们使用包含超过100万个条目的英国招聘广告数据集衡量了基于这些方法的技能要求。我们使用工资回归估计确定技能的回报。最后，我们可以通过可以解释的工资差异来比较不同的方法，假设识别的技能更好地识别了劳动力市场工资差异的较高部分。我们发现自上而下的方法的性能要比LDA模型差，因为它们只能解释约20％的工资变化，而LDA模型则解释了约45％的工资变化。
In this paper, we compare different methods to extract skill requirements from job advertisements. We consider three top-down methods that are based on expert-created dictionaries of keywords, and a bottom-up method of unsupervised topic modeling, the Latent Dirichlet Allocation (LDA) model. We measure the skill requirements based on these methods using a U.K. dataset of job advertisements that contains over 1 million entries. We estimate the returns of the identified skills using wage regressions. Finally, we compare the different methods by the wage variation they can explain, assuming that better-identified skills will explain a higher fraction of the wage variation in the labor market. We find that the top-down methods perform worse than the LDA model, as they can explain only about 20% of the wage variation, while the LDA model explains about 45% of it.
